Stack ranking performance management system in General Electric (GE)

General Electric (GE) Company is a global digital company established in 1892 by Thomas Edison, Charles A. Coffin, Elihu Thompson and Ediwin J. Howston in New York. The main business of GE is production of aircraft engines, power generation, oil and gas production equipments, medical imaging, financing and industrial equipment. With the invention of electric bulb, General Electric Co. catapulted to newer heights and it presently stands at the 8th position in the Fortune 500 list. GE‘s portfolio boasts of four major aspects of the world by building, curing, moving and powering. Today the company has close to 300,000 employees (GE, 2017). The company is also known to make famous the stack ranking technique which is used for performance evaluation of employees.

General Electric’s performance measurement system faced a major issue of transparency. Employees felt that the evaluation system did not provide feedback regarding their performance. It held them from improving their performance, as a result of which their managers fired them from their positions. Due to the company’s policy of maintaining only a certain number of employees every year, the bottom 10% performers lost their jobs without adequate reason. The company wanted to improve its overall performance and morale among the workforce with a number of methods.

Performance management system problem faced by GE

The major problem with the measurement system at General Electric was that the managers had to categorize only 20% of the total employees as ‘outstanding’, 70% as ‘high performers’ and the remaining 10% as ‘in need of improvement’ (Carol T Kulik, 2004). This system was called the ‘Stack Ranking’ or ‘Rank & Yank’ system and was introduced in late 1980s by Jack Welch. The employees falling in the criteria of ‘need for improvement’ were asked to leave the company as GE wanted to build a company with high rated employees only (Nisen, 2015b).

Due to such categorization done annually, the company fired many employees. This lead to frustration among the employees as they were not given any feedback or a chance to improve their performance (Nisen, 2015a). Since, the compensation decisions were also made on the basis of this categorization, employees were under pressure of performing better or else they would be asked to leave. GE failed to acknowledge that employees’ performance depends upon several factors other than their abilities, such as the workplace atmosphere, operational process of the company and low morale as a result of job insecurity. Finally in 2015, GE replaced this technique due to an overall negative impact on employees (Nisen, 2015a).

Effects of stack ranking technique

With the annual evaluation using stack ranking, General Electric fired 10% of its employees every year. Dismissing that amount of the workforce annually created a negative impact and low employee morale among others (Nisen, 2015b). Moreover, the employees falling in the ‘poor performers’ category sought feedback to justify the reason of categorization. As several thousand employees fell in this category, managers could not talk face to face with each and every employee and explain them the reason for their dismissal (Nisen, 2015b). This lead to more frustration as the employees felt they were fired immediately and no chance was given to them for improvement.

Solution

With the rise of technology, in 2016 former CEO of General Electric Jack Immelt initiated the design for a mobile app which could help the employees to get better feedback instantly.(Financial Post, 2016) The application or app is named as ‘[email protected]’, an acronym for Performance Development at GE. The major aim of the [email protected] is to encourage productive conversations within the company. In the app, managers have to create short-term tasks for employees and give regular feedback. Employees can also ask for instant feedback on the app (Addady, 2015). By this way, the company registered real time feedback and improvements in production. Employees can also request for meetings with their managers and such conversations can boost their morale. Currently, 25,000-30,000 people use the app (Nisen, 2015b). With the large number of employees using the app, GE was able to address the main problem of failure to give feedback.

Advantages of the new system

The major problem faced by employees of GE had was the feedback, which the new app attempted to overcome, by giving every employee feedback on real time basis (Financial Post, 2016). GE tackled problems like low morale, frustration and degraded relationships among the employees with the introduction of [email protected] With time, all employees will get familiar with the app and the company can modify it for further ease in the feedback process. Touch point meetings also give employees a sense of security as the feedback system meant they got another chance to improve themselves. For the managers, it helped them know their employees better and set goals accordingly.

Even though GE did not invent stack ranking, the system became synonymous with the company due to their long usage of the technique. Stack ranking technique and strict policies for dismissal had a very negative impact on the employees. The prolonged outcry among employees and low morale forced the company to switch to a different technique. With the introduction of the mobile app [email protected], the company eliminated the major problem of feedback and opportunities for growth. However, the barrier of the app is that the managers still have to rank or rate their employees for the compensation and promotions manually. This does not show up in the app. Also, managers have to undergo training in order to understand the new system and enable easy adoption of [email protected] The acclaim received by the employees is slow but steadily leading to the complete removal of stack ranking system from GE.

Priya is a master in business administration with majors in marketing and finance. She is fluent with data modelling, time series analysis, various regression models, forecasting and interpretation of the data. She has assisted data scientists, corporates, scholars in the field of finance, banking, economics and marketing.

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